Marcin Furtak | Searcharxiv

arXiv2025

ZipperChain: Transmuting Trusted Third-Party Services Into Trustless Atomic Broadcast

Distributed ledger technologies (DLTs) rely on distributed consensus mechanisms to reach agreement over the order of transactions and to provide immutability and availability of transaction data. Distributed consensus suffers from performance limitations of network communication between participating nodes. BLOCKY ZipperChain guarantees immutability, agreement, and availability of transaction data, but without relying on distributed consensus. Instead, its construction process transfers trust from widely-used, third-party services onto ZipperChains's correctness guarantees. ZipperChain blocks are built by a pipeline of specialized services deployed on a small number of nodes connected by a fast data center network. As a result, ZipperChain transaction throughput approaches network line speeds and block finality is on the order of 500 ms. Finally, ZipperChain infrastructure creates blocks centrally and so does not need a native token to incentivize a community of verifiers.

cs.DC↗

arXiv2025

Helping Blind People Grasp: Enhancing a Tactile Bracelet with an Automated Hand Navigation System

Grasping constitutes a critical challenge for visually impaired people. To address this problem, we developed a tactile bracelet that assists in grasping by guiding the user's hand to a target object using vibration commands. Here we demonstrate the fully automated system around the bracelet, which can confidently detect and track target and distractor objects and reliably guide the user's hand. We validate our approach in three tasks that resemble complex, everyday use cases. In a grasping task, the participants grasp varying target objects on a table, guided via the automated hand navigation system. In the multiple objects task, participants grasp objects from the same class, demonstrating our system's ability to track one specific object without targeting surrounding distractor objects. Finally, the participants grasp one specific target object by avoiding an obstacle along the way in the depth navigation task, showcasing the potential to utilize our system's depth estimations to navigate even complex scenarios. Additionally, we demonstrate that the system can aid users in the real world by testing it in a less structured environment with a blind participant. Overall, our results demonstrate that the system, by translating the AI-processed visual inputs into a reduced data rate of actionable signals, enables autonomous behavior in everyday environments, thus potentially increasing the quality of life of visually impaired people.

cs.HC↗